Computational Intelligence Models for Insurance Fraud Detection: A Review of a Decade of Research

Authors

  • Amira Kamil Ibrahim Hassan Department of Computer Science, Sudan University of Science and Technology
  • Ajith Abraham Machine Intelligence Research Labs (MIR Labs)

Keywords:

insurance fraud, fraud detection, data mining

Abstract

This paper presents a review of the literature on the application of data mining techniques for the detection of insurance fraud. Academic literature were analyzed and classified into three types of insurance fraud (automobile insurance, crop insurance and healthcare insurance) and six classes of data mining techniques (classification, regression, clustering, prediction, outlier detection, and visualization). The findings of this review clearly show that automobile insurance fraud detection have also attracted a great deal of
attention in recent years. The main data mining techniques used for insurance fraud detection are logistic models, Decision tree, the Naïve Bayes, and support vector machine.

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Published

2013-10-01

How to Cite

Amira Kamil Ibrahim Hassan, & Ajith Abraham. (2013). Computational Intelligence Models for Insurance Fraud Detection: A Review of a Decade of Research. Journal of Network and Innovative Computing, 1, 7. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/43

Issue

Vol. 1 (2013)

Section

Review

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